AI’s talent arms race is starting to look like pro sports

AI is entering a new phase where access to top talent is becoming as important as, if not more important than, compute or data. The market for AI researchers is so overheated, it’s starting to look a lot like pro sports — complete with outsized contracts and unprecedented infrastructure needs. On today’s episode of Equity, Rebecca Bellan chatted with Deedy Das, principal at Menlo Ventures. Das has seen this shift from multiple angles, first as an engineer and product leader at Google, Facebook, and AI startup Glean, and now as an investor helping technical founders figure out how to build enduring companies in this new AI landscape. Listen to the full episode to hear: Why Meta is spending billions on both compute and researchers. How compensation packages and acquisitions are warping startup hiring and retention. What motivates top researchers to leave, even when they’ve already made millions. How VCs are thinking about key-person risk in the AI era. Equity will be back Friday with our weekly news roundup, so stay tuned. Equity is TechCrunch’s flagship podcast, produced by Theresa Loconsolo, and posts every Wednesday and Friday. Subscribe to us on Apple Podcasts, Overcast, Spotify and all the casts. You also can follow Equity on X and Threads, at @EquityPod. For the full episode transcript, for those who prefer reading over listening, check out our full archive of episodes here. Learn more about your ad choices. Visit megaphone.fm/adchoices

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Anthony Ha?

  This episode of Equity is brought to you by Schneider Electric. Schneider Electric, advancing energy tech.

• 0:17Rebecca Bellan?

  Hello and welcome back to Equity, TechCrunch's flagship podcast about the business of startups. I'm Rebecca Bellan and this is the episode where we bring on industry experts to help us explore a trend in the tech world and dive deep. AI is entering a new phase where access to top talent is becoming as important or possibly more important than compute or data. The market for AI researchers is so overheated, it's starting to resemble pro sports, complete with outsized contracts, talent agents and unprecedented infrastructure needs. So today, we're talking to Didi Das, Principal at Menlo Ventures. Didi's seen this shift from multiple angles. First as an engineer and product lead at Google, Facebook and the AI startup Glean, and now as an investor helping technical founders figure out how to build enduring companies in this new AI landscape. Didi, welcome to the show.

• 1:01Speaker 02

  Thank you for having me, Rebecca. It's a pleasure to be here.

• 1:03Rebecca Bellan?

  Yeah, really excited to hear your thoughts on this. I mean, you have posted on X about this a little bit. High level, what to make of these insane contracts that we're seeing mostly come out of Meta?

• 1:15Speaker 02

  You know, it's actually pretty insane to me, too, just to see the kind of salaries that have been dished out. You know, the most recent reports people are saying, I don't know if this is true or not, have been a billion dollars over four years, up from 100 million over four years, which is

• 1:29Rebecca Bellan?

  I don't think that's true.

• 1:30Speaker 02

  Which is insane as it is. You know, I think what's really happening in the industry is engineers were always valued as an asset that is somewhat dispensable. A lot of it was because it's really hard to measure how good an engineer is. If I'm building a company, in the previous era, I would be like, hey, I need some front end engineers, maybe I need some backend engineers, maybe I need some machine learning people. I know exactly what skills I'm looking for. There's plenty of people which have those skills. And then, once they join my company, it's actually quite hard for me to tell who is the outsized performer and who isn't. There's not many signals I have. With AI, everything's sort of flipped where it's all of a sudden, there's a very specific Skill I'm hiring for, which is I need people who can train, collect data, run infrastructure,

• 2:15Speaker 02

  run inference pipelines on these large AI models and very, very few people actually have that skill. And I know how big the reward at the end of the tunnel is. So it kind of does, from a business point of view, make sense for some people to dish out these crazy salaries, almost like they were athletes. You know, there's obviously some athletes who can make or break the championship for you. A lot of companies and Meta, obviously the highlight here, believe that that's the same thing that's true for them. So it is quite interesting what we're seeing, at least in some companies with the talent. To be clear, it's not just Meta, right? If you look at the early employees of Anthropic and OpenAI, it's not like they were paid that much in salary,

• 2:56Speaker 02

  but their equity is clearly worth north of $100 million in some cases, too. You know, you are seeing these crazy outcomes, even from companies and startups as well, who will get acquired into these big companies somewhat prematurely in their journey.

• 3:09Rebecca Bellan?

  Yeah, well, I do want to talk a little bit like how earlier stage startups are faring in this environment. But do you have any insight into the types of salaries that sports players are getting? And like how those contracts are being written? Like I'm seeing NBA players can make $285 million for a four-year deal. Is that how we're kind of writing these contracts for AI engineers? Is it like you're with me for four years and you get this money? Or you help me reach this KPI and you get that money? Like, do you have any insight into how these deals are structured? And is it a perfect analogy with sports drafts?

• 3:42Speaker 02

  I don't think it's a perfect analogy. To my knowledge, the way these deals are structured is a scaled version of how general compensation would work. So you have a sign-on bonus, perhaps. You have a cash salary, which may be actually a bit lower, but then you get a lot of equity on top. So I don't believe these are all pure cash offers. You get a lot of equity on top. Usually in the previous generation, you'd have a one-year cliff on that equity. But now oftentimes they're waiving that one-year cliff. And they're going to say, hey, your equity actually starts vesting, say, monthly or weekly from your grant date. The other core difference, though, is it is at-will employment, as far as I know. So unlike athletes, there is no lock-in period for those researchers.

• 4:25Speaker 02

  I mean, the researchers can leave whenever they want. Meta can, or any other company can potentially lay them off if they would like to whenever they want.

• 4:33Rebecca Bellan?

  So if a researcher leaves, can they still get equity? Or like, are there any carve outs in these contracts to ensure that they're not just like taking the next best offer within a few months?

• 4:42Speaker 02

  To my knowledge, no. I mean, there could be kickers and incentives depending. And it is often on a case by case basis where maybe you perform well and we dangle a carrot and say, we promise you more equity or more compensation. But to my knowledge, there is no reason why you just can't leave in six months. If somebody else is willing to pay you similarly, or you want to go somewhere else. And very often, we've already seen examples of this in various companies. We saw the two people from the Claude Code team on Anthropic go to Cursor and then come back within several weeks. We've seen a lot of mobility across OpenAI, Anthropic, XAI, Meta. So there's a lot of churn there. Often people don't even stay for a year. So it is definitely, in my mind, the cases that people can move.

• 5:27Speaker 02

  There is nothing really holding you to one place, other than your desire to be there.

• 5:32Rebecca Bellan?

  Yeah, I wonder how they're handling things like trade secrets and because it's knowledge that everyone is kind of accessing and you're just really filtering it through the companies. So it must be really difficult. Yeah, I don't know what kinds of contracts about sharing information there, signing there.

• 5:46Speaker 02

  It's so interesting because like the other analogy I hear a lot is actually with high frequency trading where trade secrets are protected to come hell or high water. I mean, that's the most important thing in high frequency trading. Your entire business is that one or two trade secrets that you have that drives your alpha on the markets. And in those cases, you know, you often have garden leaves, non competes. There are some companies who are famous for lifetime non competes. So I actually do wonder whether that will make its way to the tech world and the AI world. It hasn't to my knowledge so far. I know generally it would be looked down upon for you to join a competitor,

• 6:27Speaker 02

  but I don't think there's anything legally enforceable in place for most of the AI researchers yet.

• 6:32Rebecca Bellan?

  Yeah, I can imagine that starting to crop up so you can incentivize people to kind of stay where they are. I mean, what are you seeing in terms of what's driving researchers and engineers to move companies? There's the mercenaries versus missionaries, right? There's some people who want to be with a certain company because they really vibe with that company's mission. And we don't have to talk about how much each of their missions have really shifted over the last few years. But are people going where the money is? Or are they going where they think the best culture is? Or what do you think?

• 6:58Speaker 02

  It's obviously, I think, very split between people. I think it's worth recognizing that the people in demand are usually not getting paid too poorly wherever they are.

• 7:07Rebecca Bellan?

  Yeah.

• 7:08Speaker 02

  Just to put some ballpark numbers, you know, if I've already made, call it $20 million at OpenAI, is it that lucrative for me to jump ship and have to rebuild everything for an incremental $50 million? I don't know. I think it's somewhat personal decision, even though we're talking about insane numbers. There are people who gain a lot of satisfaction by working on cutting edge things. One thing I hear a lot from AI researchers is, I've gone and I've built all this foundational stuff. I just don't want to build it again. I just want to keep pushing the needle here, and I think I make enough. And that's perfectly valid missionary type reason. I guess I take somewhat issue when people say missionary. They often mean, oh, I don't care about the money. I'm just doing it for the mission.

• 7:53Speaker 02

  That's almost never the case, right?

• 7:54Rebecca Bellan?

  No, that's not the case. That's not what we mean here on TechCrunch, I assure you.

• 7:58Speaker 02

  People are getting paid a lot of money wherever they are. And then it's really a question of like, how much does the incremental dollar mean to me versus just being able to do my best work at a certain place? And that is split. And there are some people who do follow the dollars. There are some people who do follow the mission, so to speak.

• 8:15Rebecca Bellan?

  I do wonder if some of the people, like, getting all of that kind of money and being that wealthy, and some of it is happening, you know, vaguely overnight for a lot of researchers. I wonder how much that goes to their heads and changes their outlook on life. Because as we've seen, you know, like, really wealthy people kind of lose touch with reality a lot of the time. But then also, I wonder if people who are money-driven are thinking, well, I'm going to go on to do my own startup, and I'm going to have to bootstrap that. And one day, I won't be part of this big other company, maybe.

• 8:42Speaker 02

  Actually quite interesting because you know, when I have conversations with friends of mine who are in this position, it's almost hard to put into words just what they're going through. Like, I'll give you a sample, I'm probably gonna misquote that person, but it's like, I have worked my whole life thinking that in the next thirty years, I'm going to have to work to be able to afford a house and a life. And now people are telling me I'm gonna make all of that money in the next year. That is a really hard thing for a lot of people to just come to terms with. These are not people who in many cases are the high-flying money people. I mean, people who go into AI research often are not the moneymakers. They're not people who go in driven by wealth.

• 9:24Speaker 02

  They go in driven by passion. They often did PhDs for that reason. They were almost never chasing the dollar. So when it lands up, yeah, I mean, I think it jars them in a really, really big way. And to be honest, like, having been an engineer myself, it takes time to get to their heads. There are many people who still don't realize that they're worth hundreds of millions of dollars and are like, should I Venmo you for that? I don't know. I guess I don't need to do that anymore. So that is strange. And then, of course, you mentioned, do they think about running their own company someday? In many cases, yes. I mean, and sometimes for the wrong reasons too. Because in their minds, it's like, okay, I have all of this money. I don't need to work for anybody else to make more money.

• 10:07Speaker 02

  It seems like the natural transition is to start my own company. But in some cases, they don't really know why or what they would do in that company, except more AI stuff. And so we do see a lot of that as well. And you know, sometimes it's bootstrapped, sometimes it's co-bootstrapped. They put in some and they still raise from investors. It's all over the spectrum. But yes, there is a big drive, once you've hit a certain number, for you to sort of take that next step and run your own ship. Not for everybody again, but it is a significant slice, I would say, of those people.

• 10:43Anthony Ha?

  Every company today is looking for an edge. Better insights, faster decisions, greater resilience. But as AI drives new growth, it also creates new demands on energy and infrastructure. Schneider Electric is connecting energy, automation, and digital technologies to help organizations make smarter decisions and operate more efficiently. Because the future won't be shaped by AI alone. It will be shaped by energy and industrial intelligence. Schneider Electric, advancing energy tech.

• 11:11Rebecca Bellan?

  Going back to what you said about people thinking they're going to work their whole life to be able to pay for a house and all these things. It just kind of reminds me of when ChatGPT came out and Nvidia blew up. The stock blew up and all the people who worked at Nvidia were like, I guess I can quit now. I can retire. My equity is blown up and I'm loaded, so why bother? That doesn't seem to be happening, at least on the AI engineer level, yet. But talking about startups, I am concerned about how this compensation for top AI talent is going to affect startups trying to raise their own funds in early days. I mean, even if we're looking at impressive startups with impressive valuations, like Safe Superintelligence from Ilya Sutskever and Thinking Machines from Miramirati.

• 11:55Rebecca Bellan?

  You know, Miramirati just raised a $2 billion seed round with a crazy valuation. But at the end of the day, what does that pay for, like, three researchers, right? How are new companies coming into this supposed to keep up in any way?

• 12:06Speaker 02

  It's quite difficult to be competitive. I will say that in many cases, there's just a clear separation between the kind of people they need, and it's often not AI researchers. So that's number one. Some startups are just not interested in that same kind of talent. Number two is probably, you know, a lot of researchers, like we just talked about, have already made their money. And so they're willing to try something new and interesting with a good group of people and aren't chasing the incremental dollar. And then number three, which has sort of always been the case for startups, is even 10 years ago, you were competing with a Google and a Facebook who could always afford to pay a lot more than you would. And I mean, often, the pure liquid compensation could be about 3 to 4x difference.

• 12:50Speaker 02

  And the reason they were still able to hire people is on the promise of the equity. So I think a lot of that fundamental structure continues to be the case. I can say even for myself, I mean, it's often a lot more enjoyable to just be a part of a small team, building something than to be a part of a big company. It's not for everybody, but people choose that as a lifestyle decision on the promise of equity and aren't always making pure financial decisions. So I think, yes, is it hard? Yes, is it getting harder 100%? But it's not like the dynamics have fundamentally changed in terms of startup hiring.

• 13:23Rebecca Bellan?

  Well, I mean, that's nice to hear, and I hope that you're right. But then it's not just the crazy compensation packages, right? It's also these insane acqui-hire deals, right? We've talked on the show about this before, but Windsurf, Google poached Windsurf CEO. I mean, the whole company was supposed to get bought out by OpenAI. Windsurf employees are cheering about their equity. And then Google poached Windsurf's CEO, co-founder, and research leads in a $2.4 billion reverse acqui-hire. And then days later, Cognition then acquires Windsurf. And I feel like, I haven't seen anything, maybe you can tell me what you've seen. I feel like that equity for Windsurf's team is not doing so hot, right? So both from the perspective of people who come and who take a chance on a startup for that equity and that promise of more, and also for investors.

• 14:07Rebecca Bellan?

  How do you square that when your main talent could just up and leave for a bigger paycheck?

• 14:12Speaker 02

  My understanding of that particular situation was that investors actually did get a payout. The team from Windsurf that left, of course, landed softly. And the team that was left over, I think, also landed fairly well because of the way the cap table works. My understanding, and this may not be completely verifiable or true, usually on the cap table, about 10 to 20% is for employees. And the rest is founders and the rest is investors. So in my understanding was Google bought out the investor and founder stake and then paid the researchers and engineers out for their equity share. The rest of the team now expanded their equity stake on the company.

• 14:54Speaker 02

  So what was, say, 10% now became 100% of the rest of the Windsurf company. And that was bought out for a sum that was handsome enough to give them a soft landing too. And cognition did the right thing of vesting everybody's unvested equity and paying them out. I don't know if it was a cash deal or a stock deal or what happened there. But if my understanding is correct, everyone did fairly well in that situation. Investors did well, the whole team did well, the acquirers were happy. So I don't make as much of it. And it's not the first time that we've seen it. I think it was interesting to see how some topics blow up online more than others. But similar things happened to character AI back in the day with relatively little scrutiny.

• 15:37Rebecca Bellan?

  Yeah. Scale AI as well. And then we had, you know, adept. And there's been a lot of companies where this is happening. It's like, it's big tech's way to get around antitrust by just acquiring top talent or the technology itself, which does, in a way, like, maybe it didn't happen with Windsurf, maybe it did, I'm not sure. But it does kind of leave behind, in some cases, like, I don't want to say a shell, that's like rude to the people who are still working on technology, but it could screw over some of those people. And it also could screw over some of the investors who are coming in at the early stage. So I'm wondering, like, as an investor, are you, like, when you come in at the early stage, are you working on building protection in case of talent departure, like key man clauses or anything like that? Or we should say keep people clauses. I actually don't like that phrasing.

• 16:16Speaker 02

  There are key people clauses. That's not standard. That's always been somewhat standard. In most of these cases, though, I don't think these were the cases where the investors suffered. Actually, in an IRH basis, this is a really good deal for investors because essentially what would be a, call it several billion dollar exit outcome, many, many years into the future. Now you have this $2.4 billion or whatever the number is, outcome within, say, two years of the investment. So you've actually seen a huge markup as long as the investors get paid out. There are certain cases, not this one, but there are other certain cases of acquihires where the management team is paid out separately from the cap table.

• 16:56Rebecca Bellan?

  Yeah, like inflection? Did that happen with inflection?

• 16:58Speaker 02

  I don't know the detail, but it potentially, there are certain companies where that happens. And for that, investors have a hard time with. So if you are going to pay out the management team and then not pay out the cap table. Then the investors suffer. I have not seen a whole lot of fine print around what you can do there. But eventually it ends up being whatever the board decides to do. There's always some fine print on these deals. It's very on a case by case basis, I would say.

• 17:24Rebecca Bellan?

  Yeah. Speaking about from the investor standpoint, when we look at the insane talent compensation for AI researchers and engineers, how, when you are now investing in companies, how are you doing due diligence? Are you assessing the team more and their personalities a little bit more? Are you, what kind of things are you looking at?

• 17:43Speaker 02

  I don't have a baseline to compare it to because I've only been investing as a profession for the last year and a half.

• 17:49Rebecca Bellan?

  Oh, so this is your norm.

• 17:51Speaker 02

  Yeah, this is my norm.

• 17:52Rebecca Bellan?

  Oh, no.

• 17:53Speaker 02

  But also I think coming from an, I'm an engineer myself and I'm a product person and I've always seen startups from the inside out. So for me, it's actually quite natural to care a lot about the team. I mean, I often think about startups as, you know, somebody once told me when I was joining Venture, it's like, you know, you're going to be doing a lot of analytics on these companies. You're going to be doing diligence calls, customer references, talking to people. And it can often confuse you in the middle of doing all of that because you'll see some positive signals, some negative signals. But when you're very, very confused, all you want to do is take a step back and ask a very simple question. Do I want to work for this person? And inevitably, do I want to work for this person becomes a question of team.

• 18:35Speaker 02

  And it is kind of what you're betting on, especially at the stage that I spend a lot of time investing in. The team is everything and I don't know if that's a new thing or an old thing. But whether I think the idea is good or the market is good, a good team can supersede all of that. Sometimes it is in the form of an acquisition. Obviously, it's impossible to tell when you're investing in the company, but it definitely matters. And when I think about would I work for this person, almost 80% of that decision is the team. So I do think it is a big factor.

• 19:04Rebecca Bellan?

  So what about the team though? Was it like how they work together, what their background is? Or like, are you combing their social media to see if their posts are unhinged, right? Like, how far does it go? And just so you know, you don't know if it's a new or old thing, this is an old thing. I've definitely heard this for years about the team being super important, but now we're putting like a much bigger paycheck associated with it.

• 19:21Speaker 02

  I would say there's a bunch of factors and there's not like one rubric for every team, but generally the things you want to see is, is this a leadership team that's capable of being an adult when things get tough? Are they good managers? Are they good leaders? You know, the classic, are they able to sober expectations when things are good? Are they able to lift up the team when things are bad? That's always a good sign. In terms of education and background, yeah, I mean, ideally you want to see a team where they could have gone and worked for any of these labs and they would have easily got in. That's a great example of a team that you want to put your money behind because presumably if they could get in to all of these labs and all these great companies, then there is going to be interest from all of those companies to buy them. So you definitely look at the quality of talent.

• 20:05Speaker 02

  And then of course you look at the uniqueness of talent. I mean, there's one thing to just be an exceptional team, but there's quite a different thing to have an exceptionally unique ability for the problem that you're targeting. So all of those things really, really matter, both like, how do you manage? Can you get hired by all these other companies? And are you a uniquely positioned team? And these are all things that we look at when we're investing in companies.

• 20:25Rebecca Bellan?

  Yeah. Finding that sweet spot is so vital, both for investors and of course, as we're seeing for these top AI companies. I guess it does make sense in a way that, you know, Meta and OpenAI and Anthropic, they'd be spending this top tier cash on good team members because they've put so much money into compute, into data. There's a bit of a sunk cost effect. I think that, you know, Mark Zuckerberg just came out with the latest five gigawatt AI data center. They're spending hundreds of billions of dollars on that, right? We've got OpenAI's one that's coming out. They've spent, what, $500 billion on that? There's so much money being spent on this. They can't waste that investment on not the absolute best researchers. And there's, you know, demand urgency, right? They're trying to get that first mover advantage. They need the best to get there, I guess.

• 21:11Speaker 02

  There's two ways. I would say like, actually genuinely switch sides on this. There's two ways to sort of think about all of this R and D investment. The first way is exactly what you said, which is like, yes, if we're going to be spending all this money on infra and we know that the prize here is so big, then of course it makes sense to pay up for the best talent. That is generally a completely well-respected view and it is definitely. True. But on the other side, I often look at the companies in China and that's what really bothers me. I mean, that keeps me up at night, which is how are they able to build these incredible models without doing that? You know, they're not.

• 21:43Rebecca Bellan?

  Well, they're spending money, right?

• 21:45Speaker 02

  It's unclear. It's really hard to get a good source on some of the China stuff. But I don't think they're spending the same kind of money. I would be hard pressed to believe that Chinese companies are spending a hundred million dollars even over four years for this kind of talent.

• 21:58Rebecca Bellan?

  Well, it's all open, right? They're winning the open source, open weight race. And so I think that a lot of the government is putting funds towards backing that. And because there's more of this open playground environment, they're able to iterate more quickly, even though they don't have, you know, the same access to chips, although now I think with Nvidia's latest announcement, I think they do, but.

• 22:16Speaker 02

  I think a lot about, okay, look at like DeepSeek or Kimi that's been in the news in the past. None of that team came from a AI first background, you know, they kind of just learned how to do this. Sure, you could argue, maybe, I don't know, maybe they're being paid a lot of money. I don't think they're being paid as much as probably a lot of the labs in the US.

• 22:36Rebecca Bellan?

  No, God, no.

• 22:36Speaker 02

  But it begs the question, like, why is it that the companies in America are having to pay so much for talent? Whereas the companies in China seem to be able to breed that local talent and make it competent enough to do these crazy tasks.

• 22:50Rebecca Bellan?

  It's cultural, I guess. I mean, do we actually need to be spending this much money on this crazy talent? Or could you be finding it in other pools? That's an interesting question to ask.

• 22:59Speaker 02

  I think about that one a lot.

• 23:01Rebecca Bellan?

  Well, if anyone listening has some deep insights into how China is absolutely crushing it with apparently far fewer research and way less money than US companies, please let us know. So then in America, then, like, do you think this will be the norm? Are we trend setting here? Or could it be that if China is able to succeed on certain metrics, that American companies might take a step back and think, oh, well, maybe we need to be doing it more like China?

• 23:24Speaker 02

  I, A, don't think it's even the norm right now. It is just a handful of companies.

• 23:29Rebecca Bellan?

  So you don't think this is going to be the future?

• 23:31Speaker 02

  No, I.

• 23:32Rebecca Bellan?

  We're not looking at this insane wealth gap widening even further.

• 23:36Speaker 02

  Not necessarily, because the wealth gap, the reason people are being paid this much is because when there's a disparity between the price to be made in a short amount of time and the amount of people who have the talent to get you to that price. That's like a very traditional market. As long as that gap remains, you pay up and you get the talent. Over time, there will be less prize in AI. I imagine a lot of that value will be captured by a few people and there will be a lot more talent to fill the supply. So as the supply of talent rises in AI and the demand will fall naturally over time. And once you start saturating the market, then the price gap will close. That's also, you know, coming back to that sports analogy, sports is very different because it's a very zero sum game. You only have a fixed number of teams.

• 24:21Speaker 02

  You always have a good prize and great players will always be paid a lot of money. But the real markets don't actually work like that. The prize overall diminishes over time and supply greatly increases over time. You know, you're not going to see 20 Tom Brady's, but you're going to see thousands of great AI researchers, will you?

• 24:36Rebecca Bellan?

  But I mean, they're saying it's like the supply is so limited and now we're moving into this world where people are just chat GPTing everything. They don't feel like they need to think and learn. And a lot of people coming into computer science now are thinking, what's the point? Why should I bother? I could just vibe code. Why do I need to learn all this stuff? So I'm actually not convinced that we will. I think that supply will continue to dwindle, but that's just me.

• 24:55Speaker 02

  I would, I would disagree with that. I think-

• 24:57Rebecca Bellan?

  I hope you're right, to be clear.

• 25:00Speaker 02

  Generally, humans are pretty adaptable, you know? As long as people know that big salaries are being paid for a certain kind of job, they will figure out a way to get that talent. And it's always been the case. So I don't think this now is very different. Of course, you're also right about the fact that there's more vibe coding, more distractions, more chat GPT. That's absolutely true. But, you know, I love this pithy quote that everyone likes to say these days and it resonates so well, which is, there has never been a better time to be alive for exceptional people and a worse time for average people.

• 25:30Rebecca Bellan?

  Oh, that's so true. We're in the Hunger Games, people.

• 25:32Speaker 02

  Yeah. And I think that's the same thing here. Like for me, even in my job, very regularly I meet people who are, you know, 16, 17, 18 years old. And in my head, I can barely process what they're saying because I'm- I'm like, I have this constant thought of I was so stupid at your age. I was so dumb. I had no idea what was going on. How do you possibly know all of this stuff? So you have that amazing talent on that side. But then, on the other hand, I imagine your average 17 year old probably has never even done their homework because they're all using chat GPT. So, you know, there is a disparity there. But still, I think that talent gap will narrow over time.

• 26:11Rebecca Bellan?

  Definitely. Okay. Well, that's a good place to end it as we contemplate what the next few years of the Hunger Games looks like for America and the world at large. Deedy, thank you so much for joining us. Where can our listeners find you?

• 26:23Speaker 02

  I am usually active on X. My handle is Deedy Das, D E E D Y D A S. And then I also post on LinkedIn if you prefer that.

• 26:32Rebecca Bellan?

  Cool. Great. Thank you so much. And to our listeners, you can find us on X and Blue Sky at Equity Pod. Talk to you on Friday.

• 26:43Rebecca Bellan?

  Equity is produced by Teresa Locan Solo with editing by Kel. We'd also like to thank TechCrunch's audience development team. Thank you so much for listening and we'll talk to you next time.